Dependence-Guided Service Composition for User-Centric SOA

Tsai, Wei Tek; Zhong, Peide; Bai, Xiaoying; Elston, Jay

doi:10.1109/jsyst.2013.2260947

Cited by 10 publications

(7 citation statements)

References 38 publications

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“…For what concerns services composition problems where the user preferences are known in advance, several works are proposed in the literature [6], [14], [24], [36]. Such works can be classified accordingly to the techniques used: graph search [12], [31], Pareto optimality [44], [46], constraints decomposition [3], meta-heuristic population [7], [17], [39], [42], [49], planning [8], [51], integer linear programming [47], [48], recommender system [9], [22], [25], and machine learning [13], [26], [29], [30], [40].…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, very few approaches have been proposed for solving the services composition problem in interaction with the users dynamically and without knowing the user's preferences a priori. The closest existing approaches are [9], [10], [20], [26], and [36]. In [9], Chen et al proposed to extract the user preferences on the QoS attributes in advance, with a location-aware web service recommendation system.…”

Section: Related Workmentioning

confidence: 99%

“…It uses historical information to estimate the success probability of a services' composition using results. Finally, Tsai et al [36] suggested a user-centric service composition to identify the subset of correlated services that best match the users' requirements from a designed ontology.…”

Section: Related Workmentioning

confidence: 99%

“…Although state-of-the-art approaches find the services composition workflow by knowing the users' priorities in advance [20], [26], [36], these approaches are limited and static; therefore, they can not handle the following cases: 1) the presence of the user uncertainties before the service executions, 2) unpredictable services manners such as service failures. We propose a reinforcement learning (RL)-based approach to dynamically obtain the optimal service composition, according to the user's expectations on the QoSs.…”

mentioning

confidence: 99%

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Reinforcement Learning for Interactive QoS-Aware Services Composition

Alizadeh

Osmani

Khanouche

et al. 2021

IEEE Systems Journal

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An important and challenging research problem in web of things is how to select an appropriate composition of concrete services in a dynamic and unpredictable environment. The main goal of this article is to select from all possible compositions the optimal one without knowing a priori the users' quality of service (QoS) preferences. From a theoretical point of view, we give bounds on the problem search space. As the QoS user's preferences are unknown, we propose a vector-valued MDP approach for finding the optimal QoS-aware services composition. The algorithm alternatively solves MDP with dynamic programming and learns the preferences via direct queries to the user. An important feature of the proposed algorithm is that it is able to get the optimal composition and, at the same time, limits the number of interactions with the user. Experiments on a real-world large size dataset with more than 3500 web services show that our algorithm finds the optimal composite services with around 50 interactions with the user. Index Terms-Quality of services (QoS), reinforcement learning (RL), services composition, web of things (WoT). I. INTRODUCTION T HE web of things (WoT) opens the way for the development of new intelligent applications that can be used to provide innovative and valuable services in several domains, such as smart cities, smart homes, ambient assisted living, and connected cars [11], [35]. One of the WoT challenges is to deal with services composition to ensure suitable services flexibility and customization. This is crucial to meet the evolving needs of users, which can be expressed through several parameters of quality of service (QoS) (such as response time, throughput, availability, price, popularity, etc.). Following the web service paradigm [4] services, composition problems are specified as a workflow involving abstract and concrete services.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Reinforcement Learning for Interactive QoS-Aware Services Composition

Alizadeh

Osmani

Khanouche

et al. 2021

IEEE Systems Journal

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show abstract

“…A key issue in user-centric service composition is to intelligently and effectively discover the subset of correlated services that best match the users' requirements. In the paper by Tsai et al [36], the authors propose a two-step composition process. In the first step, users choose templates from ontology with its dependence.…”

Section: Guest Editorial Advances Of Systems Research In Service Indumentioning

confidence: 99%

Guest Editorial Advances of Systems Research in Service Industry

2014

IEEE Systems Journal

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Multi-agent System Based Service Composition in the Internet of Things

Berrani¹,

Yachir²,

Djamaa³

et al. 2018

Computational Intelligence and Its Applications

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Service composition is seen as the key issue to create innovative, efficient, flexible and dynamic applications on the Internet of things (IoT). Accordingly, we propose, in this paper, an approach for IoT service composition based on multi-agent system where several agents are engaged to satisfy the user request. This approach is designed using SysML and implemented using Netlogo platform. The use-cases scenarios and extensive tests show clearly the interest, the feasibility and the suitability of the multi-agent system for service composition.

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Dependence-Guided Service Composition for User-Centric SOA

Cited by 10 publications

References 38 publications

Reinforcement Learning for Interactive QoS-Aware Services Composition

Reinforcement Learning for Interactive QoS-Aware Services Composition

Guest Editorial Advances of Systems Research in Service Industry

Multi-agent System Based Service Composition in the Internet of Things

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